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DOE OSTI2025

Low Activity Tritium Detection in CCDs Using Deep Learning Techniques

Here, this study explores the use of charge-coupled devices (CCDs) for detecting low-energy beta particles from tritium decay - a critical signal for nuclear safety, nuclear nonproliferation, and environmental monitoring. We employ a dual approach utilizing both measured CCD data and detailed Geant4 simulations. Our analysis compares classical techniques with advanced deep learning methods, including convolutional neural networks (CNNs), autoencoders trained exclusively on tritium data, and preliminary studies on boosted decision trees (BDTs). The CNN, trained on mixed signal/background datasets, demonstrates superior classification performance, while the autoencoder shows the potential of unsupervised, background-agnostic strategies when background characteristics are poorly defined. These results highlight the excellent sensitivity achievable thanks to the background rejection made possible by information-rich CCD data, paving the way for improved portable tritium monitoring.

Autoencoder↗

DOE OSTI2026

Optimization of an Energy Tuning Assembly for High Explosives Detection

The Portable Isotopic Neutron Spectroscopy (PINS) system, employs neutron-induced gamma-ray spectroscopy and provides a nondestructive method for high explosives detection. In standard operation it uses Californium-252 as a neutron source. Operating PINS with a deuterium-tritium (DT) neutron generator has some advantages over Cf-252, including lifetime and ability to produce high-energy inelastic scattering gamma rays. However, current systems using DT neutron generators suffer from a high environmental background and reduced ability to induce neutron capture, reducing spectral quality and limiting nitrogen sensitivity. Here, this study presents the development of an energy-tuning assembly (ETA) designed to optimize the DT neutron energy spectrum to increase nitrogen reaction rates in a target, thereby improving high explosive detection capabilities. A metaheuristic optimization framework, MultiGNOWEE, coupled with MCNP, was employed to generate two ETA configurations: a single-objective ETA, which maximizes nitrogen capture reactions, and a multi-objective ETA, which balances neutron capture and inelastic scattering. Simulations demonstrated the optimized configurations achieved up to a 10-fold improvement in nitrogen capture rates compared to the bare configuration. Experimental validation was conducted using a DT neutron generator and a high-purity germanium (HPGe) detector. Two prototype ETAs were constructed and assessed on a melamine simulant. Measurements demonstrated improved nitrogen detection for both prototype ETA configurations when compared to the standard system.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Design and Performance Characterization of RADICAL-Pilot on Leadership-Class Platforms

Many extreme scale scientific applications have workloads comprised of a large number of individual highperformance tasks. The Pilot abstraction decouples workload specification, resource management, and task execution via job placeholders and late-binding. As such, suitable implementations of the Pilot abstraction can support the collective execution of large number of tasks on supercomputers. We introduce RADICAL-Pilot (RP) as a portable, modular and extensible Pilot enabled runtime system. We describe RP's design, architecture and implementation. We characterize its performance and show its ability to scalably execute workloads comprised of tens of thousands heterogeneous tasks on DOE and NSF leadership-class HPC platforms. Specifically, we investigate RP's weak/strong scaling with CPU/GPU, single/multi core, (non)MPI tasks and python functions when using most of ORNL Summit and TACC Frontera. RADICAL-Pilot can be used stand-alone, as well as the runtime for third-party workflow systems.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Uncertainty Visualization of Critical Points of 2D Scalar Fields for Parametric and Nonparametric Probabilistic Models

This paper presents a novel end-to-end framework for closed-form computation and visualization of critical point uncertainty in 2D uncertain scalar fields. Critical points are fundamental topological descriptors used in the visualization and analysis of scalar fields. The uncertainty inherent in data (e.g., observational and experimental data, approximations in simulations, and compression), however, creates uncertainty regarding critical point positions. Uncertainty in critical point positions, therefore, cannot be ignored, given their impact on downstream data analysis tasks. Here, in this work, we study uncertainty in critical points as a function of uncertainty in data modeled with probability distributions. Although Monte Carlo (MC) sampling techniques have been used in prior studies to quantify critical point uncertainty, they are often expensive and are infrequently used in production-quality visualization software. We, therefore, propose a new end-to-end framework to address these challenges that comprises a threefold contribution. First, we derive the critical point uncertainty in closed form, which is more accurate and efficient than the conventional MC sampling methods. Specifically, we provide the closed-form and semianalytical (a mix of closed-form and MC methods) solutions for parametric (e.g., uniform, Epanechnikov) and nonparametric models (e.g., histograms) with finite support. Second, we accelerate critical point probability computations using a parallel implementation with the VTK-m library, which is platform portable. Finally, we demonstrate the integration of our implementation with the ParaView software system to demonstrate near-real-time results for real datasets.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

FunM2C: A Filter for Uncertainty Visualization of Multivariate Data on Multi-Core Devices

Uncertainty visualization is an emerging research topic in data visualization because neglecting uncertainty in visualization can lead to inaccurate assessments. In this paper, we study the propagation of multivariate data uncertainty in visualization. Although there have been a few advancements in probabilistic uncertainty visualization of multivariate data, three critical challenges remain to be addressed. First, the state-of-the-art probabilistic uncertainty visualization framework is limited to bivariate data (two variables). Second, existing uncertainty visualization algorithms use computationally intensive techniques and lack support for cross-platform portability. Third, as a consequence of the computational expense, integration into production visualization tools is impractical. In this work, we address all three issues and make a threefold contribution. First, we take a step to generalize the state-of-the-art probabilistic framework for bivariate data to multivariate data with an arbitrary number of variables. Second, through utilization of VTK-m’s shared-memory parallelism and cross-platform compatibility features, we demonstrate acceleration of multivariate uncertainty visualization on different many-core architectures, including OpenMP and AMD GPUs. Third, we demonstrate the integration of our algorithms with the ParaView software. We demonstrate the utility of our algorithms through experiments on multivariate simulation data with three and four variables.

Hari, Gautam↗

DOE OSTI2026

Efficient Probabilistic Visualization of Local Divergence of 2D Vector Fields with Independent Gaussian Uncertainty

This work focuses on visualizing uncertainty of local divergence of two-dimensional vector fields. Divergence is one of the fundamental attributes of fluid flows, as it can help domain scientists analyze potential positions of sources (positive divergence) and sinks (negative divergence) in the flow. However, uncertainty inherent in vector field data can lead to erroneous divergence computations, adversely impacting downstream analysis. While Monte Carlo (MC) sampling is a classical approach for estimating divergence uncertainty, it suffers from slow convergence and poor scalability with increasing data size and sample counts. Thus, we present a two-fold contribution that tackles the challenges of slow convergence and limited scalability of the MC approach. (1) We derive a closed-form approach for highly efficient and accurate uncertainty visualization of local divergence, assuming independently Gaussian-distributed vector uncertainties. (2) We further integrate our approach into Viskores, a platform-portable parallel library, to accelerate uncertainty visualization. In our results, we demonstrate significantly enhanced efficiency and accuracy of our serial analytical (speed-up up to 1946×) and parallel Viskores (speed-up up to 19698×) algorithms over the classical serial MC approach. We also demonstrate qualitative improvements of our probabilistic divergence visualizations over traditional mean-field visualization, which disregards uncertainty. We validate the accuracy and efficiency of our methods on wind forecast and ocean simulation datasets.

Ouermi, Timbwaoga [University of Utah]↗

DOE OSTI2022

Analysis of Validating and Verifying OpenACC Compilers 3.0 and Above

OpenACC is a high-level directive-based parallel programming model that can manage the sophistication of heterogeneity in architectures and abstract it from the users. The portability of the model across CPUs and accelerators has gained the model a wide variety of users. This means it is also crucial to analyze the reliability of the compilers’ implementations. To address this challenge, the OpenACC Validation and Verification team has proposed a validation testsuite to verify the OpenACC implementations across various compilers with an infrastructure for a more streamlined execution. This paper will cover the following aspects: (a) the new developments since the last publication on the testsuite, (b) outline the use of the infrastructure, (c) discuss tests that highlight our workflow process, (d) analyze the results from executing the testsuite on various systems, and (e) outline future developments.

Jarmusch, Aaron↗

DOE OSTI2022

KokkACC: Enhancing Kokkos with OpenACC

Template metaprogramming is gaining popularity as a high-level solution for achieving performance portability on heterogeneous computing resources. Kokkos is a representative approach that offers programmers high-level abstractions for generic programming while most of the device-specific code generation and optimizations are delegated to the compiler through template specializations. For this, Kokkos provides a set of device-specific code specializations in multiple back ends, such as CUDA and HIP. Unlike CUDA or HIP, OpenACC is a high-level and directive-based programming model. This descriptive model allows developers to insert hints (pragmas) into their code that help the compiler to parallelize the code. The compiler is responsible for the transformation of the code, which is completely transparent to the programmer. This paper presents an OpenACC back end for Kokkos: KokkACC. As an alternative to Kokkos’s existing device-specific back ends, KokkACC is a multi-architecture back end providing a high-productivity programming environment enabled by OpenACC’s high-level and descriptive programming model. Moreover, we have observed competitive performance; in some cases, KokkACC is faster (up to 9×) than NVIDIA’s CUDA back end and much faster than OpenMP’s GPU offloading back end. This work also includes implementation details and a detailed performance study conducted with a set of mini-benchmarks (AXPY and DOT product) and three mini-apps (LULESH, miniFE and SNAP, a LAMMPS proxy mini-app).

Valero Lara, Pedro↗

DOE OSTI2024

High‐Resolution Direct Push NMR Tools for Groundwater Investigations

Abstract A small‐diameter nuclear magnetic resonance (NMR) technology optimized for use with direct push (DP) and cone penetrometer test (CPT) drilling has been developed. The DP NMR tool can be deployed through 2.25‐in. diameter DP and CPT drilling rods allowing high‐resolution NMR logging measurements to be acquired during retraction of drill rods. DP NMR technology runs from a person‐portable battery‐powered control unit and provides significantly higher resolution in both the spatial (vertical) dimension and in the time domain of the NMR measurement than previously available NMR technology. In this study, we summarize the development of two different DP NMR tools and demonstrate their application at different sites within the United States. We believe that this technology can provide a leap forward in adoption of NMR technology for high‐resolution hydrological and geophysical investigations in groundwater resources and environmental remediation applications.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Cryogenic optical beam steering for superconducting device calibration

We have developed a calibration system based on a micro-electromechanical systems (MEMS) mirror that is capable of delivering an optical beam over a wavelength range of 180 -- 2000 nm (0.62 -- 6.89 eV) in a sub-Kelvin environment. This portable, integrated system can steer the beam over a $\sim$3 cm $\times$ 3 cm area on the surface of any sensor with a precision of $\sim$100 $μ$m, enabling characterization of device response as a function of position. This fills a critical need in the landscape of calibration tools for sub-Kelvin devices, including those used for dark matter detection and quantum computing. These communities have a shared goal of understanding the impact of ionizing radiation on device performance, which can be pursued with our system. This paper describes the design of the first-generation calibration system and the results from successfully testing its performance at room temperature and 20 mK.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2026

Binary pseudo-random array standard for extreme ultraviolet lithography tool characterization

Extreme ultraviolet (EUV) imaging tools play a crucial role in EUV lithography. Achieving high accuracy in EUV metrology is essential for advanced semiconductor manufacturing. A thorough characterization of the instrumentation in use is required. Binary pseudo-random arrays (BPRAs) are an established standard for calibrating and characterizing optical instruments in the frequency domain. Here, we expand the BPRA standard to applications in EUV imaging. To extend the technology to the EUV spectral range, a high-resolution BPRA target with the smallest feature size of 40 nm is developed. The EUV BPRA target establishes an in situ and portable calibration and alignment standard for EUV imaging. The target is patterned by means of electron-beam lithography, using a nickel absorber with a thickness of 39 nm. The substrate is a 4″ silicon wafer with a molybdenum/silicon multilayer coating. To demonstrate the efficacy of the target and develop the instrument calibration protocol, the target is imaged on the Sharp Hyper-NA Actinic Reticle Review Project EUV mask microscope. Power spectral density (PSD) data are presented. The characteristics of the imaging system are imprinted on the PSD. The modulation transfer function is extracted from the PSD data. A partially coherent imaging model is used as a reference to the experimental results.

BPRA↗

DOE OSTI2025

Improved field assessments of chemicals and explosives using high-resolution gamma-ray spectroscopy with a tagged neutron interrogation system

A field-portable tagged neutron interrogation system is being developed for detection of explosives and chemical warfare agents by prompt γ-ray neutron activation analysis. The system combines an associated particle imaging deuterium-tritium neutron generator with a high-purity germanium γ-ray detector. Fine spatial resolution neutron and α detectors enable precise mapping of characteristic γ-ray emission distributions onto high-clarity neutron radiographic images. A narrow coincidence gate associated with each tagged neutron ensures a large signal-to-background ratio, thus significantly improving sensitivity to the chemical compositions of the items being assessed. Recent experimental results recorded from test objects involving mock explosives and other chemicals are presented.

Bucher, Brian [Idaho National Laboratory (INL)]↗

DOE OSTI2025

Coherent diffraction imaging in the undergraduate laboratory

We present an undergraduate optics instructional laboratory designed to teach skills relevant to a broad range of modern scientific and technical careers. In this laboratory project, students image a custom aperture using coherent diffraction imaging, while learning principles and skills related to digital image processing and computational imaging, including multidimensional Fourier analysis, iterative phase retrieval, noise reduction, finite dynamic range, and sampling considerations. After briefly reviewing these imaging principles, we describe the required experimental materials and setup for this project. Our experimental apparatus is both inexpensive and portable, and a software application we developed for interactive data analysis is freely available.

Porter, J. Nicholas↗

DOE OSTI2020

Three dimensions, two microscopes, one code: Automatic differentiation for x-ray nanotomography beyond the depth of focus limit

Conventional tomographic reconstruction algorithms assume that one has obtained pure projection images, involving no within-specimen diffraction effects nor multiple scattering. Advances in x-ray nanotomography are leading toward the violation of these assumptions, by combining the high penetration power of x-rays, which enables thick specimens to be imaged, with improved spatial resolution that decreases the depth of focus of the imaging system. We describe a reconstruction method where multiple scattering and diffraction effects in thick samples are modeled by multislice propagation and the 3D object function is retrieved through iterative optimization. We show that the same proposed method works for both full-field microscopy and for coherent scanning techniques like ptychography. Our implementation uses the optimization toolbox and the automatic differentiation capability of the open-source deep learning package TensorFlow, demonstrating a straightforward way to solve optimization problems in computational imaging with flexibility and portability.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2020

High-sensitivity in vivo contrast for ultra-low field magnetic resonance imaging using superparamagnetic iron oxide nanoparticles

Magnetic resonance imaging (MRI) scanners operating at ultra-low magnetic fields (ULF; <10 mT) are uniquely positioned to reduce the cost and expand the clinical accessibility of MRI. A fundamental challenge for ULF MRI is obtaining high-contrast images without compromising acquisition sensitivity to the point that scan times become clinically unacceptable. Here, we demonstrate that the high magnetization of superparamagnetic iron oxide nanoparticles (SPIONs) at ULF makes possible relaxivity- and susceptibility-based effects unachievable with conventional contrast agents (CAs). We leverage these effects to acquire high-contrast images of SPIONs in a rat model with ULF MRI using short scan times. This work overcomes a key limitation of ULF MRI by enabling in vivo imaging of biocompatible CAs. These results open a new clinical translation pathway for ULF MRI and have broader implications for disease detection with low-field portable MRI scanners.

42 ENGINEERING↗

DOE OSTI2024

High-precision chemical quantum sensing in flowing monodisperse microdroplets

A method is presented for high-precision chemical detection that integrates quantum sensing with droplet microfluidics. Using nanodiamonds (ND) with fluorescent nitrogen-vacancy (NV) centers as quantum sensors, rapidly flowing microdroplets containing analyte molecules are analyzed. A noise-suppressed mode of optically detected magnetic resonance is enabled by pairing controllable flow with microwave control of NV electronic spins, to detect analyte-induced signals of a few hundredths of a percent of the ND fluorescence. Using this method, paramagnetic ions in droplets are detected with low limit-of-detection using small analyte volumes, with exceptional measurement stability over >10 3 s. In addition, these droplets are used as microconfinement chambers by co-encapsulating ND quantum sensors with various analytes such as single cells, suggesting wide-ranging applications including single-cell metabolomics and real-time intracellular measurements from bioreactors. Important advances are enabled by this work, including portable chemical testing devices, amplification-free chemical assays, and chemical imaging tools for probing reactions within microenvironments.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

A New Class of AMG Interpolation Methods Based on Matrix-Matrix Multiplications

A new class of distance-two interpolation methods for algebraic multigrid (AMG) that can be formulated in terms of sparse matrix-matrix multiplications is presented and analyzed. Compared with similar distance-two prolongation operators, the proposed algorithms exhibit improved efficiency and portability to various computing platforms, since they allow one to easily exploit existing high-performance sparse matrix kernels. The new interpolation methods have been implemented in hypre, a widely used parallel multigrid solver library. With the proposed interpolations, the overall time of hypre's BoomerAMG setup can be considerably reduced, while sustaining equivalent, sometimes improved, convergence rates. Numerical results for a variety of test problems on parallel machines are presented that support the superiority of the proposed interpolation operators over the existing ones in hypre.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Tensor Network Quantum Virtual Machine for Simulating Quantum Circuits at Exascale

The numerical simulation of quantum circuits is an indispensable tool for development, verification, and validation of hybrid quantum-classical algorithms intended for near-term quantum co-processors. The emergence of exascale high-performance computing (HPC) platforms presents new opportunities for pushing the boundaries of quantum circuit simulation. Here, we present a modernized version of the Tensor Network Quantum Virtual Machine (TNQVM) that serves as the quantum circuit simulation backend in the eXtreme-scale ACCelerator (XACC) framework. The new version is based on the scalable tensor network processing library ExaTN (Exascale Tensor Networks). It provides multiple configurable quantum circuit simulators that perform either an exact quantum circuit simulation via the full tensor network contraction or an approximate simulation via a suitably chosen tensor factorization scheme. Upon necessity, stochastic noise modeling from real quantum processors is incorporated into the simulations by modeling quantum channels with Kraus tensors. By combining the portable XACC quantum programming frontend and the scalable ExaTN numerical processing backend, we introduce an end-to-end virtual quantum development environment that can scale from laptops to future exascale platforms. We report initial benchmarks of our framework, which include a demonstration of the distributed execution, incorporation of quantum decoherence models, and simulation of the random quantum circuits used for the certification of quantum supremacy on Google’s Sycamore superconducting architecture.

Nguyen, Thien↗